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SAS is a statistical software that was developed by SAS institute for managing data and performing different kinds of analytics that include multivariate analytics, predictive analytics, and all kinds of advanced analytics. In this article, we will mainly discuss Base SAS. The three main functions of Base SAS are- Managing Data, Can be used as a Programming language, and a powerful tool for analysis of data and generating reports.
It is used both in industries and in academics. Banking, marketing, healthcare, health insurance, automotive, capital markets are some of the sectors to name a few.
The following figure shows us how a raw data is converted to a final analysis in SAS:
A SAS data set can be used to create another SAS data can be done in the following way:
The following figure shows how a SAS interface looks like.
The Left side is the Navigation pane and the right side is the work area.
On the right side, there are three options Code, Log and Results
Code: In code, we write the proc statements. Proc statements in SAS acts as the procedure statements. We write run to run the program. For example, we can write the following:
proc print data = Aircraft;
*Aircraft is the name of the dataset.
Log: The log area shows any error or warnings after the code is executed. Following is how a log area would show after a program is executed.
Results: The result area is the output area that gives us the final result.
Structure of a SAS data set:
A SAS data set is of a rectangular shape with rows and columns, where rows are called observations and columns are called variables.
There are two types of libraries in SAS - Temporary and Permanent. A Library in SAS is used as a storage location to save the programs.
Temporary library also known as Work library is created for a one-time session. After the session ends, the temporary library gets deleted.
Permanent library is permanent in nature and that exists even when the session gets over. Permanent SAS libraries can be created using SAS utilities or by writing codes in the editor window.
In order to work with raw data in SAS, we first need to convert raw data to SAS data. We first need to see how the data we want to view are arranged. The three input statements that are available are - list, column, and formatted. The structure of the raw data needs to be examined properly, that is to see whether data are aligned in columns or unaligned, or whether there is a delimiter like a comma, etc. in numeric data or if there is a blank. We use different coding for different kinds of situations. For example, if raw data is delimited by comma we will use dlm=',' in the program.
Infile and file statements in SAS acts as a connection between an external file and SAS.
The following are some of the basic statements in SAS and their uses:
Set: It reads observations from the available SAS data.
Input: Reads raw data from in-stream data lines or an external data file.
Title: The title we want to be printed in the output.
Keep: Helps to keep only the selected variables that are required for the study.
Drop: Helps to drop or remove the variables that we don't want to include.
If/else: Test if a condition is true, when a condition is true i.e. if statement then action is to be done, otherwise if the condition is false, i.e. the else if statement then an alternative action is to be done.
Delete: It helps to delete a particular observation from the output.
We can use the common operators +,-,*, and / for the basic mathematical operations. The functions round and sum are used to round a figure and find the sum of the desired variables respectively.
Character variables can be letters, special characters, or numbers. We may use the SCAN function for extracting a portion of the character value. For aligning the value to the left we use the LEFT function. For concatenating variable values we use the symbol (||) or (!!) in SAS programming. For removing unwanted space we use the TRIM function. Other functions that are used for character variables include RIGHT, COMPBL, SUBSTR, INDEX, UPCASE, etc.
Every date is a unique number in SAS. The starting date in SAS is set as January 1, 1960, all dates before this date are negative numbers. We use YEARCUTOFF= when we are working with a data having 100 years span. MMDDYY8 reads dates written in the form mm/dd/yy, MMDDYY10 reads dates written in the form mm/dd/yyyy, DATE7 reads data in dd/mm/yy form, and Date9 reads data in dd/mm/yyyy form. FORMAT statement in SAS is often used for date values.
It combines two or more than two SAS data sets one after another into a single one. Concatenating can be done using the SET statement or the APPEND procedure. SET statement is used for any number of data sets, while APPEND uses two datasets.
It combines two sorted data sets into a single sorted one. It uses the SET statement and the BY statement in a DATA step.
It combines observations from two or more data sets into a single one. The MERGE statement is used in the data steps. The BY statement is used along with the MERGE statement.
Updating involves two data sets - one data set has the original information known as master dataset, the other one has the new information known as the transaction dataset. We use the UPDATE statement in order to update SAS dataset.
Modify statement modifies observations in the original dataset. Unlike update, modify does not copy the file. We use the MODIFY statement in SAS in order to modify SAS dataset.
Various statistical analysis can be performed in SAS. The following gives details of the correlation analysis in SAS
In correlation analysis, we deal with the relationship between two variables. Correlation coefficient lies between -1 and +1. If there a perfect linear positive relationship then the correlation coefficient is +1, whereas for perfect linear negative relationship then the correlation coefficient is -1. On the other hand if there is no-linear relationship then the correlation coefficient is 0.
The following shows how a correlation coefficient can be obtained from SAS:
PROC CORR DATA=dataset
The above will consider all the variables of the dataset and a correlation matrix will be obtained as the output.
PROC CORR DATA=sample;
VAR price demand;
The above will consider two variables - price and demand of the dataset and a correlation coefficient between these two variables will be obtained as an output.
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